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          联邦学习初理解
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<p>开头一段是故事，可跳过看正文</p>
</blockquote>
<p>几年前师兄让我研究一下为深度学习训练提供云服务时，如何解决用户的数据隐私顾虑，精度允许有损耗。</p>
<p>当时一腔热血展开调研，对着CNN结构苦思冥想，不给数据怎么训练？给了数据怎么隐私？</p>
<p>然后顺理成章的钻进了数据加密的死胡同。原想着“加密-&gt;训练-&gt;解密”做成端到端或许行得通，跑了一波实验只能得到雪花点。发现一个无比牛叉的发明——同态加密，啃了好几天终于略有些理解，却是框架难实现、模型难收敛、图像难处理。</p>
<p>在同态加密的死胡同里挣扎了一番，还是数学能力不足放弃了，感觉是个无解的问题。</p>
<p>今年突然发现了“联邦学习”这个名词，科研界与工业界早已炒得火热，第一反应就是“啊？这样训练也能用？也能刷论文？也能做产品？”</p>
<p>好吧感觉这么多年还是不够懂科研。想起那个水笔的故事——墨水只能灌那么高，再高会漏墨，怎么办呢，把笔芯就生产成那么高墨水的就好了，完全能用啊。</p>
<p>如今坑都填差不多了，剩下的要么是人不想做的，要么是无比难搞的。</p>
<p>这世上精明人那么多，哪有那么多舒服的方向给你。而且也给过你机会了。</p>
<p>静下心来不要挑三拣四，总能做出点东西。</p>
<span id="more"></span>
<h2 id="联邦学习是什么">联邦学习是什么</h2>
<p>大数据时代，数据为王，机器学习模型或好或坏，丢几十T数据进去，都能给你练出个NB模型，预测用户习惯，精准推送文章、商品、广告，换来的都是流量，都是真金白银。</p>
<p>可用户不开心啊，我的聊天记录，我的朋友圈，我的联系人列表，都是我的隐私，你凭什么拿去机器学习。感到被监控，感到可怕...</p>
<p>好的，法律来了，国内外都有，保护用户隐私。</p>
<p>互联网公司郁闷了，这可咋整。</p>
<p>好好好，不拿你数据了，借你手机cpu用你自己的数据跑几个特征值给我，我看不到你的隐私，还依然给你提供智能的自动化服务，双赢，如何。</p>
<p>联邦学习的雏形就这么来了，比较典型的开始是谷歌2016年预测Android设备文本输入的模型训练中，保持数据在设备本地，服务器只收集设备按机器学习算法求出的局部梯度，通过服务器合并，在数学上逼近全局训练效果的计算方案。</p>
<p>再往后，更丰富的需求和方案也来了：</p>
<ul>
<li>不同的企业都有数据，合在一起训练的模型不是更好。但是数据都是钱啊，凭什么给。互换数据？问问我的用户答应不答应。</li>
<li>不同的部门有同一批用户的不同方面的数据，银行有存款信息，医院有病例信息，结合起来可以有更完整的用户模型，但是...</li>
</ul>
<p>要么是部门财产，要么是用户隐私，反正数据都拿出来一起跑个好模型的乌托邦是不存在的。</p>
<p>机器学习一般把数据看作一个一个的包含多个特征值的数据，一个放一行的话，可以放进一个Excel表格里，一行是一个数据，每列是数据的一个特征值。</p>
<p>那么</p>
<ul>
<li>横向联邦学习：不同企业的数据是不同的行，他们可能包含了不同的类别，把这些数据以联邦学习的方式训练联合模型，会支持更丰富的类别或更好的分类泛化性能。</li>
<li>纵向联邦学习：不同部门的数据是不同的列，即每个数据都包含了用户重叠或不重叠的若干特征，以联邦学习的方式训练联合模型，会基于更多的特征给出更精准的结果。</li>
</ul>
<h2 id="联邦学习研究些什么">联邦学习研究些什么</h2>
<h3 id="通讯成本高">通讯成本高</h3>
<p>不是本地一个GPU从硬盘里拿了就训练，而是要各个终端频繁地发送梯度信息，下载模型更新信息。</p>
<p>在保证相同模型精度前提下，考虑减少通讯次数、模型更新时间等</p>
<h3 id="统计学异质性statistical-heterogeneity">统计学异质性（Statistical
Heterogeneity）</h3>
<p>这词也没找到官方翻译，说“异构性”感觉格局小了。。其实单词直译看起来还不错。</p>
<p>这个问题在普通机器学习中就有，比如训练mnist
<code>0</code>到<code>9</code>的手写数字识别，如果加入训练的数据是一堆<code>0</code>，然后一堆<code>1</code>，然后...，在训练完一堆<code>9</code>之后，你会发现模型可能预测啥都是<code>9</code>。每堆数据的分布是不一样的。</p>
<p>那怎么办？传统机器学习把数据<strong>随机打乱</strong>一下就好了。这样你随便拿起“一堆”数据，他们就会有个统计学分布（比如画个柱状图统计不同数字个数）。不同“堆”得到的分布都很接近。</p>
<p>按概率论说，每次随便拿起一个数字是几的概率是相互独立的，即先拿起一个数是<code>0</code>，不会影响再拿起一个数是几的概率。</p>
<p>又分布接近，又相互独立，嗯，独立同分布（Independent and Identically
Distributed，IID），这样的数据，才能在机器学习的熔炉里均衡地炼出较靠谱的解。</p>
<p>那前面扎堆的<code>0</code>、扎堆的<code>1</code>...扎堆的<code>9</code>就是非独立同分布了（Non-IID）。</p>
<p>传统机器学习可以随机打乱，但联邦学习我们没法把数据集合在一起处理，你的手机里全是<code>0</code>，我的手机里全是<code>1</code>，麻烦就大了。</p>
<p>再者，手机运算速度、网络环境、网络波动，都给不同方向传给服务器的数据带来各种不确定性，归根到底都影响了数据的分布。</p>
<p>所以联邦学习最大的困难就是悬丝诊脉，哦不，是碰不到数据还要依赖数据的分布。</p>
<h3 id="数据安全">数据安全</h3>
<p>既然要数据隐私，那这要考虑的就太多了，数据怎么不泄露？即使不发数据，发的信息会不会足够猜到数据本身是什么？</p>
<p>如果要依赖“大”数据，往服务器发信息的终端一定都是“善良”的吗？</p>
<p>任何事情牵扯到互联网，环节就会非常多，数据发送、接收、模型聚合、下发，都要顾虑一个安全问题，隐私安全，模型安全，数据安全...</p>
<h3 id="应用落地">应用落地</h3>
<p>有理论不一定能用，把理论用起来产生经济价值也有一系列要解决的问题。</p>
<h3 id="需求与优化">需求与优化</h3>
<p>异步训练？模型验证？无监督？区块链？...</p>
<h2 id="我想做点什么">我想做点什么</h2>
<p>统计学异质性是个老话题，哪怕是在联邦学习中。如前文所说，好填的坑都被人填完了，难啃的骨头啃一啃吧，总有些能改进的地方，对应用落地还是有些帮助。</p>
<hr />
<h2 id="参考">参考</h2>
<ul>
<li>[1] YANG Q, LIU Y, CHEN T, et al. Federated machine learning:
Concept and applications[J]. ACM Trans. Intell. Syst. Technol., 2019,
10(2): 12:1­12:19.</li>
<li>[2] LI T, SAHU A K, TALWALKAR A, et al. Federated learning:
Challenges, methods, and future directions[J]. IEEE Signal Processing
Magazine, 2020, 37: 50­60.</li>
<li>[3] LI L, FAN Y, TSE M, et al. A review of applications in federated
learning[J]. Computers &amp; Industrial Engineering, 2020, 149:
106854.</li>
<li>[4] LI T, SAHU A K, ZAHEER M, et al. Federated optimization in
heterogeneous networks [C]//DHILLON I S, PAPAILIOPOULOS D S, SZE V.
Proceedings of Machine Learning and Systems 2020, MLSys 2020, Austin,
TX, USA, March 2­4, 2020. mlsys.org, 2020.</li>
</ul>

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